Robust Fault Diagnosis in Electric Drives Using Machine Learning
ARMY TANK AUTOMOTIVE RESEARCH DEVELOPMENT AND ENGINEERING CENTER WARREN MI
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The power electronics inverter can be considered as the weakest link in an electric drive system, hence the focus of this research work is on the detection of fault conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points which in turn are fed to an electric drive model to generate signals for training an artificial neural network that has the capability of robustly classifying multiple classes of faults in the electric drive system. Six faulted models for the inverter and the motor, and a normally functioning model were used to generate various fault condition data for machine learning. The technique is viable for accurate, reliable and fast fault detection in electric drives.
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